Most Liveable Cities

Dot Map

Photo by Alex Rainer on Unsplash

The streets of Vienna are paved with culture, the streets of other cities with asphalt…
— Karl Kraus

Ingest

city ranks

Wrangle

enrich with coordinates

df <- df %>% select(Rank, City, Country)

all_cities <- read.csv("archetypes/most-liveable-cities/ne_10m_populated_places.csv", header = TRUE, stringsAsFactors = FALSE, encoding = "utf-8")
all_cities <- all_cities %>% select(NAME, NAMEASCII, ADM0NAME, LATITUDE, LONGITUDE)

df_coords <- merge(df, all_cities, by.x = c("City", "Country"), by.y = c("NAMEASCII", "ADM0NAME"))
head(df_coords, n = 10)

Wrangle

random sample for labels

sample_labels <- sample_n(df_coords, 25)
sample_labels

Analyze

create bin groups by rank, and labels for legend

df_bins <- df_coords %>% mutate(GROUP = ntile(df_coords$Rank, 6))
head(df_bins, n = 10)

df_bin_labels <- df_bins %>%
  group_by(GROUP) %>%
  summarise(
    bmin = min(Rank, na.rm = T),
    bmax = max(Rank, na.rm = T),
    count = n()
  ) %>%
  mutate(label = paste0("[ ", bmin, "-", bmax, " ]"))

# df_bin_labels

Base map

natural earth

ne_world <- ne_countries(scale = "small", returnclass = "sf")
ne_world <- filter(ne_world, iso_a3 != "ATA")

View

cities, ranks, and labels

theme_opts <- theme(
  text = element_text(family = "inconsolata"), 
  plot.title = element_text(color = "black", size = 14, face = "bold", family = "inconsolata"),
  plot.subtitle = element_text(color = "black", size = 12, family = "inconsolata"),
  plot.caption = element_text(color = "#555555", size = 8, family = "inconsolata"),
  plot.background = element_blank(),
  panel.grid = element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  panel.background=element_rect(fill="white", colour="white"),
  panel.border = element_blank(),
  axis.text = element_blank(),
  axis.line = element_blank(),
  axis.ticks = element_blank(),
  axis.title = element_blank(),
  legend.position = "top",
  legend.title = element_blank()
)

bin_palette <- c(
  "1" = "#254055", # most liveable
  "2" = "#7597b4",
  "3" = "#9ac4b8",
  "4" = "#c1cbba",
  "5" = "#e2d0ae",
  "6" = "#dba63b"  # least liveable
)

v1 <- ggplot(data = ne_world) +
  geom_sf(fill="#d9e0e5", color="white", size = 0.5) +
  geom_point( data = df_bins, aes(x=LONGITUDE, y=LATITUDE, fill = as.factor(GROUP)), shape = 21, size = 2, color = "#333333") +
  geom_text_repel(data = sample_labels, aes(x = LONGITUDE, y = LATITUDE, label = paste0(City, " [ ", Rank, " ]")), 
                  size = 4, hjust = 0.5, vjust = 0.5, family = "inconsolata", fontface = "bold",
                  seed = 42, force = 5, force_pull = 0, box.padding = 0.75, min.segment.length = 0, direction = "y", 
                  color = "black", bg.color = "white", bg.r = 0.15) +
  scale_fill_manual(values = bin_palette, labels = df_bin_labels$label) +
  theme_bw() +
  labs(x=NULL,
       y=NULL,
       title = "Quality of Living City Ranking", 
       subtitle="color by rank group") +
  theme_opts

girafe(ggobj = v1, width_svg = 16, height_svg = 8.25,
       options = list(opts_sizing(rescale = TRUE, width = 1.0)))

References

Citations and data sources

Narrative: The Economist, Graphic detail, GO
Data: Mercer, Quality of living city ranking 2019, GO